# normalizeChIP: Normalization of ChIP-seq count data. (deprecated) In epigenomix: Epigenetic and gene transcription data normalization and integration with mixture models

## Description

This method is deprecated. Use normalize instead. This method implements some normalization approaches for ChIP-seq data after counting reads within regions or bins. Similar methods are often applied to RNA-seq data after counting reads within genes.

## Usage

 1 normalizeChIP(object, method) 

## Arguments

 object A ChIPseqSet object as generated by summarizeReads method Normalization method, either "scaleTotal", "scaleRegion", "scaleMedianRegion" or "quantile"

## Details

The following normalization methods are implemented:

1. scaleTotalSamples are scaled by a factor such that all samples have the same number of reads (the median number of reads observed accross all samples before normalization). All reads are used for calculating the scaling factor.

2. scaleRegionSamples are scaled by a factor such that all samples have the same number of reads (the median number of reads observed accross all samples before normalization). In contrast to scaleTotal, only reads falling into the regions (genes, promoters) that were used to create the given ChIPseqSet object are used for calculating the scaling factor. Hence, the sum of all columns of the returned ChIPseqSet are equal after applying this method.

3. scaleMedianRegionThe scaling factor s_j for the j-th sample is defined as:

s_j = median_i \frac{k_{ij}}{∏_{v=1}^m k_{iv}}

k_{ij} is the value of region i in sample j. See Anders and Huber (2010) for details.

4. quantileQuantile normalization is applied to the ChIP-seq values such that each sample has the same cdf after normalization.

## Value

An ChIPseqSet-class object with normalized ChIP-seq values.

## Author(s)

Hans-Ulrich Klein ([email protected]muenster.de

## References

Anders and Huber; Differential expression analysis for sequence count data; Genome Biology 2010, 11:R106

summarizeReads
  1 2 3 4 5 6 7 8 9 10 11 12  chip <- matrix(c(5,6,5,6,10,12,10,12), nrow=4, dimnames=list(c("f1", "f2", "f3", "f4"), c("s1", "s2"))) rowRanges <- GRanges(IRanges(start=c(10, 20, 30, 40), end=c(11, 21, 31, 41)), seqnames=c("1", "1", "1", "1")) names(rowRanges) = rownames(chip) chipDf <- DataFrame(totalCount=c(100, 100), row.names=colnames(chip)) cSet <- ChIPseqSet(chipVals=chip, rowRanges=rowRanges, colData=chipDf) chipVals(cSet) chipVals(normalize(cSet, method="scaleMedianRegion")) chipVals(normalize(cSet, method="quantile"))